# Mastering Diverse Domains through World Models

**Authors:** Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap  
**arXiv:** [2301.04104](https://arxiv.org/abs/2301.04104)  
**Date:** 2023-01-10  
**Summary page:** https://www.summarizepaper.com/en/paper/2301.04104v1/

## Key points

- DreamerV3 is a general and scalable algorithm based on world models that outperforms previous approaches in reinforcement learning across various domains.
- Achieving general intelligence in AI requires the ability to solve tasks across multiple domains, which current reinforcement learning algorithms struggle with due to the resources and knowledge required for tuning them for new tasks.
- Previous algorithms like PPO and SAC show promise but require significant tuning and experience to perform well.
- MuZero has achieved high performance but at the cost of complex components like MCTS with UCB exploration, while Gato is limited to tasks where expert data is available.
- DreamerV3 demonstrates mastery across diverse environments with fixed hyperparameters and from scratch, showcasing favorable scaling properties where larger models lead to higher data efficiency and final performance.
- DreamerV3 excels in handling continuous and discrete actions, visual and low-dimensional inputs, 2D and 3D worlds, different data budgets, reward frequencies, and scales.
- DreamerV3 is the first algorithm capable of collecting diamonds in Minecraft without human data or curricula, a significant achievement in artificial intelligence.
- The scalability of DreamerV3 makes it broadly applicable in reinforcement learning for effectively tackling hard decision-making problems.

## Summary

The paper "Mastering Diverse Domains through World Models" presents DreamerV3, a general and scalable algorithm based on world models that outperforms previous approaches in reinforcement learning across various domains. The goal of achieving general intelligence in AI requires the ability to solve tasks across multiple domains, which current reinforcement learning algorithms struggle with due to the resources and knowledge required to tune them for new tasks. Previous algorithms like PPO and SAC have shown promise but require significant tuning and experience to perform well. MuZero has achieved high performance but at the cost of complex components like MCTS with UCB exploration. Gato fits one large model to expert demonstrations but is limited to tasks where expert data is available. In contrast, DreamerV3 demonstrates mastery across diverse environments with fixed hyperparameters and from scratch. DreamerV3 showcases favorable scaling properties, where larger models directly translate to higher data efficiency and final performance. It excels in handling continuous and discrete actions, visual and low-dimensional inputs, 2D and 3D worlds, different data budgets, reward frequencies, and scales. Notably, DreamerV3 is the first algorithm capable of collecting diamonds in Minecraft without human data or curricula, a significant achievement in artificial intelligence. The scalability of DreamerV3 makes it broadly applicable in reinforcement learning, allowing for tackling hard decision-making problems effectively. The algorithm's success in mastering diverse domains highlights its potential for future investigations in AI research.